Publikationen

Autor	Terhörst, Philipp; Huber, Marco; Damer, Naser; Kirchbuchner, Florian; Raja, Kiran; Kuijper, Arjan
Datum	2023
Art	Journal Article
Abstrakt	In this work, we introduce the concept of pixel-level face image quality that determines the utility of single pixels in a face image for recognition. We propose a training-free approach to assess the pixel-level qualities of a face image given an arbitrary face recognition network. To achieve this, a model-specific quality value of the input image is estimated and used to build a sample-specific quality regression model. Based on this model, quality-based gradients are back-propagated and converted into pixel-level quality estimates. In the experiments, we qualitatively and quantitatively investigated the meaningfulness of our proposed pixel-level qualities based on real and artificial disturbances and by comparing the explanation maps on faces incompliant with the ICAO standards. In all scenarios, the results demonstrate that the proposed solution produces meaningful pixel-level qualities enhancing the interpretability of the face image and its quality. The code is publicly available.
ISSN	2637-6407
Publisher	IEEE
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/440296

Pixel-Level Face Image Quality Assessment for Explainable Face Recognition